XGBoost Classification: Customer Conversion Prediction

Overview

Built an XGBoost classification model to predict customer conversion and identify key drivers influencing whether users place an order. Conducted exploratory data analysis (EDA) to uncover behavioral and transactional patterns. Performed cohort analysis to measure customer retention over time, identify drop-off points, and understand lifecycle behavior. Delivered insights to improve the restaurant discovery experience and boost overall conversion rates.

Project Highlights

• Machine Learning: XGBoost classification model for predicting customer conversion
• Exploratory Data Analysis: Uncovered behavioral and transactional patterns
• Cohort Analysis: Measured customer retention and identified drop-off points
• Business Impact: Actionable insights to improve conversion rates and user experience

Technologies Used

• Python
• XGBoost
• Pandas
• NumPy
• Matplotlib/Seaborn
• Scikit-learn

Key Findings

• Identified key drivers influencing customer conversion
• Measured customer retention trends over time
• Discovered critical drop-off points in the customer lifecycle
• Provided recommendations to enhance the restaurant discovery experience

Project Structure

├── notebooks/   
├── deck/              
└── README.md           

Getting Started

Prerequisites

pip install xgboost pandas numpy matplotlib seaborn scikit-learn

Usage

1. Clone the repository
2. Install dependencies
3. Run the Jupyter notebooks in the notebooks/ directory
4. Review the analysis and model results

Results

The XGBoost model successfully predicted customer conversion with high accuracy, enabling targeted interventions to improve user engagement and conversion rates.

Future Work

• Implement real-time prediction pipeline
• Explore additional feature engineering
• Test ensemble methods for improved performance
• Develop A/B testing framework for recommendations

Contact

For questions or collaboration opportunities, feel free to reach out via GitHub.